Аннотации:
Metaheuristic algorithms are widely used in solving optimiza-tion problems. In this paper, a new metaheuristic algorithm called Skill Optimization Algorithm (SOA) is proposed to solve optimization problems. The fundamental inspiration in designing SOA is human efforts to acquire and improve skills. Various stages of SOA are mathematically modeled in two phases, including: (i) exploration, skill acquisition from experts and (ii) exploitation, skill improvement based on practice and individual effort. The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types. The optimization results show that SOA, by balancing exploration and exploitation, is able to provide good performance and appro-priate solutions for optimization problems. In addition, the performance of SOA in optimization is compared with ten metaheuristic algorithms to evalu-ate the quality of the results obtained by the proposed approach. Analysis and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achieves much more competitive results.
